Overview

Dataset statistics

Number of variables22
Number of observations929
Missing cells3830
Missing cells (%)18.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory159.8 KiB
Average record size in memory176.1 B

Variable types

DateTime1
Categorical3
Numeric16
Unsupported2

Alerts

Latitude (deg.) has a high cardinality: 571 distinct values High cardinality
Longitude (deg.) has a high cardinality: 670 distinct values High cardinality
vy is highly correlated with Velocity (km/s)High correlation
vz is highly correlated with LatHigh correlation
Total Radiated Energy (J) is highly correlated with Calculated Total Impact Energy (kt)High correlation
Calculated Total Impact Energy (kt) is highly correlated with Total Radiated Energy (J)High correlation
quarter is highly correlated with monthHigh correlation
month is highly correlated with quarterHigh correlation
Lat is highly correlated with vzHigh correlation
Long is highly correlated with vxHigh correlation
Velocity (km/s) is highly correlated with vx and 1 other fieldsHigh correlation
vx is highly correlated with Velocity (km/s) and 1 other fieldsHigh correlation
Latitude (deg.) has 190 (20.5%) missing values Missing
Longitude (deg.) has 190 (20.5%) missing values Missing
Altitude (km) has 458 (49.3%) missing values Missing
Velocity (km/s) has 653 (70.3%) missing values Missing
vx has 653 (70.3%) missing values Missing
vy has 653 (70.3%) missing values Missing
vz has 653 (70.3%) missing values Missing
Lat has 190 (20.5%) missing values Missing
Long has 190 (20.5%) missing values Missing
Total Radiated Energy (J) is highly skewed (γ1 = 29.63127787) Skewed
Calculated Total Impact Energy (kt) is highly skewed (γ1 = 28.79922147) Skewed
Latitude (deg.) is uniformly distributed Uniform
Longitude (deg.) is uniformly distributed Uniform
Peak Brightness Date/Time (UT) has unique values Unique
date is an unsupported type, check if it needs cleaning or further analysis Unsupported
time is an unsupported type, check if it needs cleaning or further analysis Unsupported
hour has 33 (3.6%) zeros Zeros
minute has 18 (1.9%) zeros Zeros
second has 11 (1.2%) zeros Zeros
weekday has 117 (12.6%) zeros Zeros

Reproduction

Analysis started2022-09-22 18:13:12.997477
Analysis finished2022-09-22 18:14:03.587007
Duration50.59 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Distinct929
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
Minimum1988-04-15 03:03:10
Maximum2022-08-21 16:35:49
2022-09-22T14:14:03.904605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T14:14:04.129311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Latitude (deg.)
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct571
Distinct (%)77.3%
Missing190
Missing (%)20.5%
Memory size7.4 KiB
28.0N
 
4
52.0S
 
3
1.0N
 
3
50.2S
 
3
28.1N
 
3
Other values (566)
723 

Length

Max length5
Median length5
Mean length4.801082544
Min length3

Characters and Unicode

Total characters3548
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique433 ?
Unique (%)58.6%

Sample

1st row6.3S
2nd row35.0S
3rd row6.0S
4th row44.8S
5th row40.5S

Common Values

ValueCountFrequency (%)
28.0N4
 
0.4%
52.0S3
 
0.3%
1.0N3
 
0.3%
50.2S3
 
0.3%
28.1N3
 
0.3%
18.9N3
 
0.3%
31.1S3
 
0.3%
22.0N3
 
0.3%
21.5S3
 
0.3%
7.8N3
 
0.3%
Other values (561)708
76.2%
(Missing)190
 
20.5%

Length

2022-09-22T14:14:04.337884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28.0n4
 
0.5%
3.2n3
 
0.4%
52.0s3
 
0.4%
4.1s3
 
0.4%
14.0n3
 
0.4%
24.4s3
 
0.4%
3.2s3
 
0.4%
46.9n3
 
0.4%
52.8n3
 
0.4%
41.8s3
 
0.4%
Other values (561)708
95.8%

Most occurring characters

ValueCountFrequency (%)
.737
20.8%
S374
10.5%
N365
10.3%
2270
 
7.6%
3261
 
7.4%
1257
 
7.2%
4249
 
7.0%
5215
 
6.1%
8178
 
5.0%
6173
 
4.9%
Other values (3)469
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2072
58.4%
Uppercase Letter739
 
20.8%
Other Punctuation737
 
20.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2270
13.0%
3261
12.6%
1257
12.4%
4249
12.0%
5215
10.4%
8178
8.6%
6173
8.3%
7166
8.0%
0164
7.9%
9139
6.7%
Uppercase Letter
ValueCountFrequency (%)
S374
50.6%
N365
49.4%
Other Punctuation
ValueCountFrequency (%)
.737
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2809
79.2%
Latin739
 
20.8%

Most frequent character per script

Common
ValueCountFrequency (%)
.737
26.2%
2270
 
9.6%
3261
 
9.3%
1257
 
9.1%
4249
 
8.9%
5215
 
7.7%
8178
 
6.3%
6173
 
6.2%
7166
 
5.9%
0164
 
5.8%
Latin
ValueCountFrequency (%)
S374
50.6%
N365
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.737
20.8%
S374
10.5%
N365
10.3%
2270
 
7.6%
3261
 
7.4%
1257
 
7.2%
4249
 
7.0%
5215
 
6.1%
8178
 
5.0%
6173
 
4.9%
Other values (3)469
13.2%

Longitude (deg.)
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct670
Distinct (%)90.7%
Missing190
Missing (%)20.5%
Memory size7.4 KiB
64.6W
 
4
175.0E
 
3
37.2W
 
3
21.0E
 
3
56.4E
 
3
Other values (665)
723 

Length

Max length6
Median length5
Mean length5.381596752
Min length3

Characters and Unicode

Total characters3977
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique607 ?
Unique (%)82.1%

Sample

1st row51.5E
2nd row78.4E
3rd row86.9W
4th row2.9W
5th row76.6E

Common Values

ValueCountFrequency (%)
64.6W4
 
0.4%
175.0E3
 
0.3%
37.2W3
 
0.3%
21.0E3
 
0.3%
56.4E3
 
0.3%
147.6W2
 
0.2%
52.2W2
 
0.2%
31.7W2
 
0.2%
25.7E2
 
0.2%
51.5E2
 
0.2%
Other values (660)713
76.7%
(Missing)190
 
20.5%

Length

2022-09-22T14:14:04.513179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
64.6w4
 
0.5%
37.2w3
 
0.4%
21.0e3
 
0.4%
56.4e3
 
0.4%
175.0e3
 
0.4%
18.8w2
 
0.3%
175.3e2
 
0.3%
53.1e2
 
0.3%
6.5e2
 
0.3%
174.4w2
 
0.3%
Other values (660)713
96.5%

Most occurring characters

ValueCountFrequency (%)
.738
18.6%
1560
14.1%
E378
9.5%
W361
9.1%
6243
 
6.1%
7241
 
6.1%
5230
 
5.8%
4229
 
5.8%
3226
 
5.7%
2223
 
5.6%
Other values (3)548
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2500
62.9%
Uppercase Letter739
 
18.6%
Other Punctuation738
 
18.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1560
22.4%
6243
9.7%
7241
9.6%
5230
9.2%
4229
9.2%
3226
9.0%
2223
 
8.9%
9191
 
7.6%
0188
 
7.5%
8169
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
E378
51.2%
W361
48.8%
Other Punctuation
ValueCountFrequency (%)
.738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3238
81.4%
Latin739
 
18.6%

Most frequent character per script

Common
ValueCountFrequency (%)
.738
22.8%
1560
17.3%
6243
 
7.5%
7241
 
7.4%
5230
 
7.1%
4229
 
7.1%
3226
 
7.0%
2223
 
6.9%
9191
 
5.9%
0188
 
5.8%
Latin
ValueCountFrequency (%)
E378
51.2%
W361
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.738
18.6%
1560
14.1%
E378
9.5%
W361
9.1%
6243
 
6.1%
7241
 
6.1%
5230
 
5.8%
4229
 
5.8%
3226
 
5.7%
2223
 
5.6%
Other values (3)548
13.8%

Altitude (km)
Real number (ℝ≥0)

MISSING

Distinct173
Distinct (%)36.7%
Missing458
Missing (%)49.3%
Infinite0
Infinite (%)0.0%
Mean36.12908705
Minimum14.5
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:04.694620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14.5
5-th percentile22.2
Q129.5
median34.8
Q340.7
95-th percentile59.1
Maximum74
Range59.5
Interquartile range (IQR)11.2

Descriptive statistics

Standard deviation10.45789242
Coefficient of variation (CV)0.2894590833
Kurtosis1.620836554
Mean36.12908705
Median Absolute Deviation (MAD)5.5
Skewness1.125621539
Sum17016.8
Variance109.3675138
MonotonicityNot monotonic
2022-09-22T14:14:04.878502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3734
 
3.7%
33.315
 
1.6%
31.514
 
1.5%
40.711
 
1.2%
35.211
 
1.2%
29.610
 
1.1%
3810
 
1.1%
2610
 
1.1%
32.49
 
1.0%
328
 
0.9%
Other values (163)339
36.5%
(Missing)458
49.3%
ValueCountFrequency (%)
14.51
 
0.1%
15.21
 
0.1%
16.71
 
0.1%
171
 
0.1%
18.71
 
0.1%
191
 
0.1%
19.12
0.2%
19.51
 
0.1%
204
0.4%
20.41
 
0.1%
ValueCountFrequency (%)
742
0.2%
721
0.1%
711
0.1%
701
0.1%
691
0.1%
68.51
0.1%
66.61
0.1%
662
0.2%
651
0.1%
64.51
0.1%

Velocity (km/s)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct138
Distinct (%)50.0%
Missing653
Missing (%)70.3%
Infinite0
Infinite (%)0.0%
Mean17.86268116
Minimum9.8
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:05.099833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.8
5-th percentile11.675
Q114.1
median16.85
Q319.925
95-th percentile28.825
Maximum49
Range39.2
Interquartile range (IQR)5.825

Descriptive statistics

Standard deviation5.614547862
Coefficient of variation (CV)0.3143171964
Kurtosis6.127713005
Mean17.86268116
Median Absolute Deviation (MAD)2.95
Skewness1.952971679
Sum4930.1
Variance31.52314769
MonotonicityNot monotonic
2022-09-22T14:14:05.308496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.26
 
0.6%
16.96
 
0.6%
15.95
 
0.5%
12.95
 
0.5%
14.95
 
0.5%
18.15
 
0.5%
13.65
 
0.5%
12.25
 
0.5%
14.15
 
0.5%
19.24
 
0.4%
Other values (128)225
 
24.2%
(Missing)653
70.3%
ValueCountFrequency (%)
9.81
 
0.1%
10.91
 
0.1%
11.12
0.2%
11.21
 
0.1%
11.31
 
0.1%
11.42
0.2%
11.53
0.3%
11.63
0.3%
11.72
0.2%
11.83
0.3%
ValueCountFrequency (%)
491
0.1%
44.81
0.1%
42.31
0.1%
36.51
0.1%
35.71
0.1%
32.11
0.1%
31.91
0.1%
31.71
0.1%
31.41
0.1%
30.21
0.1%

vx
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct205
Distinct (%)74.3%
Missing653
Missing (%)70.3%
Infinite0
Infinite (%)0.0%
Mean-0.1731884058
Minimum-35.4
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative141
Negative (%)15.2%
Memory size7.4 KiB
2022-09-22T14:14:05.524905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-35.4
5-th percentile-16.725
Q1-8.5
median-0.85
Q38.425
95-th percentile17.7
Maximum27.8
Range63.2
Interquartile range (IQR)16.925

Descriptive statistics

Standard deviation10.91147213
Coefficient of variation (CV)-63.00347923
Kurtosis-0.2296358398
Mean-0.1731884058
Median Absolute Deviation (MAD)8.25
Skewness-0.09174214764
Sum-47.8
Variance119.060224
MonotonicityNot monotonic
2022-09-22T14:14:05.720330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-105
 
0.5%
-8.93
 
0.3%
9.63
 
0.3%
9.83
 
0.3%
10.33
 
0.3%
0.23
 
0.3%
1.53
 
0.3%
-15.33
 
0.3%
-2.43
 
0.3%
-2.53
 
0.3%
Other values (195)244
 
26.3%
(Missing)653
70.3%
ValueCountFrequency (%)
-35.41
0.1%
-29.11
0.1%
-28.21
0.1%
-27.81
0.1%
-22.81
0.1%
-22.41
0.1%
-19.31
0.1%
-18.91
0.1%
-18.61
0.1%
-18.21
0.1%
ValueCountFrequency (%)
27.81
0.1%
25.21
0.1%
23.11
0.1%
21.51
0.1%
21.31
0.1%
20.41
0.1%
20.21
0.1%
19.21
0.1%
19.11
0.1%
18.62
0.2%

vy
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct204
Distinct (%)73.9%
Missing653
Missing (%)70.3%
Infinite0
Infinite (%)0.0%
Mean-1.969927536
Minimum-43.5
Maximum31.2
Zeros0
Zeros (%)0.0%
Negative161
Negative (%)17.3%
Memory size7.4 KiB
2022-09-22T14:14:05.936864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-43.5
5-th percentile-16.7
Q1-9.925
median-2.75
Q35.55
95-th percentile15.525
Maximum31.2
Range74.7
Interquartile range (IQR)15.475

Descriptive statistics

Standard deviation11.07963577
Coefficient of variation (CV)-5.624387478
Kurtosis0.445109557
Mean-1.969927536
Median Absolute Deviation (MAD)7.9
Skewness-5.323669932 × 10-5
Sum-543.7
Variance122.7583287
MonotonicityNot monotonic
2022-09-22T14:14:06.134382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12.14
 
0.4%
2.24
 
0.4%
23
 
0.3%
-2.53
 
0.3%
-5.83
 
0.3%
-93
 
0.3%
5.33
 
0.3%
-3.33
 
0.3%
63
 
0.3%
8.12
 
0.2%
Other values (194)245
 
26.4%
(Missing)653
70.3%
ValueCountFrequency (%)
-43.51
0.1%
-40.41
0.1%
-24.41
0.1%
-23.71
0.1%
-23.51
0.1%
-23.21
0.1%
-22.71
0.1%
-21.71
0.1%
-19.61
0.1%
-18.32
0.2%
ValueCountFrequency (%)
31.21
0.1%
25.81
0.1%
25.31
0.1%
23.51
0.1%
23.41
0.1%
181
0.1%
17.71
0.1%
17.31
0.1%
171
0.1%
16.81
0.1%

vz
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct200
Distinct (%)72.5%
Missing653
Missing (%)70.3%
Infinite0
Infinite (%)0.0%
Mean-0.9047101449
Minimum-31.2
Maximum27
Zeros0
Zeros (%)0.0%
Negative138
Negative (%)14.9%
Memory size7.4 KiB
2022-09-22T14:14:06.356527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-31.2
5-th percentile-17.55
Q1-9.2
median0
Q36.625
95-th percentile16.125
Maximum27
Range58.2
Interquartile range (IQR)15.825

Descriptive statistics

Standard deviation10.46033615
Coefficient of variation (CV)-11.56208561
Kurtosis-0.2324554032
Mean-0.9047101449
Median Absolute Deviation (MAD)7.9
Skewness-0.1155471762
Sum-249.7
Variance109.4186323
MonotonicityNot monotonic
2022-09-22T14:14:06.548717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.54
 
0.4%
-10.33
 
0.3%
4.63
 
0.3%
1.43
 
0.3%
1.73
 
0.3%
-10.53
 
0.3%
9.73
 
0.3%
-5.53
 
0.3%
3.23
 
0.3%
8.83
 
0.3%
Other values (190)245
 
26.4%
(Missing)653
70.3%
ValueCountFrequency (%)
-31.21
0.1%
-28.71
0.1%
-27.71
0.1%
-261
0.1%
-23.91
0.1%
-22.81
0.1%
-20.91
0.1%
-20.81
0.1%
-20.21
0.1%
-19.61
0.1%
ValueCountFrequency (%)
271
0.1%
23.91
0.1%
23.71
0.1%
221
0.1%
19.21
0.1%
191
0.1%
18.81
0.1%
18.61
0.1%
17.81
0.1%
17.51
0.1%

Total Radiated Energy (J)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct308
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.597491927 × 1011
Minimum2 × 1010
Maximum3.75 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:06.759865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2 × 1010
5-th percentile2.1 × 1010
Q13.2 × 1010
median6.4 × 1010
Q31.46 × 1011
95-th percentile1.038 × 1012
Maximum3.75 × 1014
Range3.7498 × 1014
Interquartile range (IQR)1.14 × 1011

Descriptive statistics

Standard deviation1.241189707 × 1013
Coefficient of variation (CV)16.33683483
Kurtosis893.4633855
Mean7.597491927 × 1011
Median Absolute Deviation (MAD)3.8 × 1010
Skewness29.63127787
Sum7.05807 × 1014
Variance1.540551889 × 1026
MonotonicityNot monotonic
2022-09-22T14:14:07.135722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 × 101031
 
3.3%
2.1 × 101026
 
2.8%
2.7 × 101024
 
2.6%
2.2 × 101023
 
2.5%
2.5 × 101019
 
2.0%
3 × 101019
 
2.0%
2.3 × 101018
 
1.9%
3.2 × 101017
 
1.8%
2.8 × 101016
 
1.7%
3.8 × 101015
 
1.6%
Other values (298)721
77.6%
ValueCountFrequency (%)
2 × 101031
3.3%
2.1 × 101026
2.8%
2.2 × 101023
2.5%
2.3 × 101018
1.9%
2.4 × 101015
1.6%
2.5 × 101019
2.0%
2.6 × 101012
 
1.3%
2.7 × 101024
2.6%
2.8 × 101016
1.7%
2.9 × 101012
 
1.3%
ValueCountFrequency (%)
3.75 × 10141
0.1%
3.13 × 10131
0.1%
2 × 10132
0.2%
1.82 × 10131
0.1%
1.04 × 10131
0.1%
1 × 10131
0.1%
7.58 × 10121
0.1%
7.56 × 10121
0.1%
7.41 × 10121
0.1%
7.26 × 10121
0.1%

Calculated Total Impact Energy (kt)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct140
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.234397201
Minimum0.073
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:07.352417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.073
5-th percentile0.076
Q10.11
median0.2
Q30.42
95-th percentile2.4
Maximum440
Range439.927
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation14.69863174
Coefficient of variation (CV)11.90753813
Kurtosis858.3391682
Mean1.234397201
Median Absolute Deviation (MAD)0.108
Skewness28.79922147
Sum1146.755
Variance216.0497752
MonotonicityNot monotonic
2022-09-22T14:14:07.544471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1141
 
4.4%
0.1337
 
4.0%
0.1233
 
3.6%
0.132
 
3.4%
0.07331
 
3.3%
0.1429
 
3.1%
0.1626
 
2.8%
0.07626
 
2.8%
0.09524
 
2.6%
0.07923
 
2.5%
Other values (130)627
67.5%
ValueCountFrequency (%)
0.07331
3.3%
0.07626
2.8%
0.07923
2.5%
0.08218
1.9%
0.08615
1.6%
0.08919
2.0%
0.09212
 
1.3%
0.09524
2.6%
0.09816
1.7%
0.132
3.4%
ValueCountFrequency (%)
4401
 
0.1%
491
 
0.1%
332
0.2%
301
 
0.1%
182
0.2%
143
0.3%
132
0.2%
101
 
0.1%
9.81
 
0.1%
9.51
 
0.1%

date
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size7.4 KiB

year
Real number (ℝ≥0)

Distinct33
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.006459
Minimum1988
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:07.725497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1988
5-th percentile1996
Q12003
median2009
Q32016
95-th percentile2021
Maximum2022
Range34
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.049547619
Coefficient of variation (CV)0.004006730583
Kurtosis-1.072998846
Mean2009.006459
Median Absolute Deviation (MAD)7
Skewness-0.1300580841
Sum1866367
Variance64.79521686
MonotonicityDecreasing
2022-09-22T14:14:07.899068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
201545
 
4.8%
200545
 
4.8%
201942
 
4.5%
202041
 
4.4%
201839
 
4.2%
200439
 
4.2%
200637
 
4.0%
200837
 
4.0%
200037
 
4.0%
201237
 
4.0%
Other values (23)530
57.1%
ValueCountFrequency (%)
19881
 
0.1%
19901
 
0.1%
19911
 
0.1%
19932
 
0.2%
199413
 
1.4%
199521
2.3%
199633
3.6%
199721
2.3%
199813
 
1.4%
199930
3.2%
ValueCountFrequency (%)
202231
3.3%
202132
3.4%
202041
4.4%
201942
4.5%
201839
4.2%
201729
3.1%
201633
3.6%
201545
4.8%
201433
3.6%
201324
2.6%

quarter
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
4
242 
1
241 
2
229 
3
217 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters929
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4242
26.0%
1241
25.9%
2229
24.7%
3217
23.4%

Length

2022-09-22T14:14:08.072548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T14:14:08.250452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4242
26.0%
1241
25.9%
2229
24.7%
3217
23.4%

Most occurring characters

ValueCountFrequency (%)
4242
26.0%
1241
25.9%
2229
24.7%
3217
23.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number929
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4242
26.0%
1241
25.9%
2229
24.7%
3217
23.4%

Most occurring scripts

ValueCountFrequency (%)
Common929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4242
26.0%
1241
25.9%
2229
24.7%
3217
23.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4242
26.0%
1241
25.9%
2229
24.7%
3217
23.4%

month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.443487621
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:08.398974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.493726382
Coefficient of variation (CV)0.5422104592
Kurtosis-1.255475866
Mean6.443487621
Median Absolute Deviation (MAD)3
Skewness0.02026977513
Sum5986
Variance12.20612403
MonotonicityNot monotonic
2022-09-22T14:14:08.534273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1087
9.4%
482
8.8%
182
8.8%
281
8.7%
1180
8.6%
779
8.5%
378
8.4%
576
8.2%
1275
8.1%
871
7.6%
Other values (2)138
14.9%
ValueCountFrequency (%)
182
8.8%
281
8.7%
378
8.4%
482
8.8%
576
8.2%
671
7.6%
779
8.5%
871
7.6%
967
7.2%
1087
9.4%
ValueCountFrequency (%)
1275
8.1%
1180
8.6%
1087
9.4%
967
7.2%
871
7.6%
779
8.5%
671
7.6%
576
8.2%
482
8.8%
378
8.4%

day
Real number (ℝ≥0)

Distinct31
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.60279871
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:08.688990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.789101838
Coefficient of variation (CV)0.563302905
Kurtosis-1.195043125
Mean15.60279871
Median Absolute Deviation (MAD)8
Skewness0.01785747633
Sum14495
Variance77.24831112
MonotonicityNot monotonic
2022-09-22T14:14:08.853970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1843
 
4.6%
940
 
4.3%
2239
 
4.2%
1537
 
4.0%
737
 
4.0%
435
 
3.8%
1235
 
3.8%
2134
 
3.7%
2834
 
3.7%
2734
 
3.7%
Other values (21)561
60.4%
ValueCountFrequency (%)
131
3.3%
232
3.4%
330
3.2%
435
3.8%
522
2.4%
633
3.6%
737
4.0%
829
3.1%
940
4.3%
1023
2.5%
ValueCountFrequency (%)
3116
1.7%
3027
2.9%
2925
2.7%
2834
3.7%
2734
3.7%
2629
3.1%
2528
3.0%
2425
2.7%
2327
2.9%
2239
4.2%

time
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size7.4 KiB

hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.26264801
Minimum0
Maximum23
Zeros33
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:09.027196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.861618159
Coefficient of variation (CV)0.6092366692
Kurtosis-1.186079666
Mean11.26264801
Median Absolute Deviation (MAD)6
Skewness0.04797692972
Sum10463
Variance47.08180376
MonotonicityNot monotonic
2022-09-22T14:14:09.183695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1247
 
5.1%
1346
 
5.0%
1746
 
5.0%
1145
 
4.8%
445
 
4.8%
344
 
4.7%
144
 
4.7%
2243
 
4.6%
642
 
4.5%
741
 
4.4%
Other values (14)486
52.3%
ValueCountFrequency (%)
033
3.6%
144
4.7%
241
4.4%
344
4.7%
445
4.8%
533
3.6%
642
4.5%
741
4.4%
832
3.4%
937
4.0%
ValueCountFrequency (%)
2331
3.3%
2243
4.6%
2138
4.1%
2040
4.3%
1929
3.1%
1828
3.0%
1746
5.0%
1639
4.2%
1535
3.8%
1433
3.6%

minute
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.82992465
Minimum0
Maximum59
Zeros18
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:09.367705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median29
Q344
95-th percentile55.6
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.096239
Coefficient of variation (CV)0.5930032496
Kurtosis-1.183500086
Mean28.82992465
Median Absolute Deviation (MAD)15
Skewness0.004383867206
Sum26783
Variance292.281388
MonotonicityNot monotonic
2022-09-22T14:14:09.565800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1626
 
2.8%
5123
 
2.5%
3023
 
2.5%
3822
 
2.4%
1922
 
2.4%
621
 
2.3%
4020
 
2.2%
220
 
2.2%
2419
 
2.0%
3119
 
2.0%
Other values (50)714
76.9%
ValueCountFrequency (%)
018
1.9%
115
1.6%
220
2.2%
317
1.8%
412
1.3%
514
1.5%
621
2.3%
719
2.0%
818
1.9%
98
 
0.9%
ValueCountFrequency (%)
5913
1.4%
589
 
1.0%
5714
1.5%
5611
1.2%
5516
1.7%
5414
1.5%
5312
1.3%
529
 
1.0%
5123
2.5%
5019
2.0%

second
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.82561895
Minimum0
Maximum59
Zeros11
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:09.779545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median32
Q346
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.3042547
Coefficient of variation (CV)0.5613595216
Kurtosis-1.185251252
Mean30.82561895
Median Absolute Deviation (MAD)15
Skewness-0.09144824317
Sum28637
Variance299.4372309
MonotonicityNot monotonic
2022-09-22T14:14:09.987856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2825
 
2.7%
3824
 
2.6%
4123
 
2.5%
5922
 
2.4%
4520
 
2.2%
5520
 
2.2%
5219
 
2.0%
5119
 
2.0%
5319
 
2.0%
3219
 
2.0%
Other values (50)719
77.4%
ValueCountFrequency (%)
011
1.2%
117
1.8%
213
1.4%
314
1.5%
413
1.4%
513
1.4%
616
1.7%
716
1.7%
810
1.1%
915
1.6%
ValueCountFrequency (%)
5922
2.4%
5817
1.8%
5715
1.6%
5615
1.6%
5520
2.2%
5412
1.3%
5319
2.0%
5219
2.0%
5119
2.0%
5015
1.6%

weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.952637244
Minimum0
Maximum6
Zeros117
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2022-09-22T14:14:10.158527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.939804258
Coefficient of variation (CV)0.6569734435
Kurtosis-1.191439994
Mean2.952637244
Median Absolute Deviation (MAD)2
Skewness0.04560684658
Sum2743
Variance3.762840559
MonotonicityNot monotonic
2022-09-22T14:14:10.450545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1152
16.4%
3143
15.4%
4142
15.3%
2133
14.3%
5124
13.3%
6118
12.7%
0117
12.6%
ValueCountFrequency (%)
0117
12.6%
1152
16.4%
2133
14.3%
3143
15.4%
4142
15.3%
5124
13.3%
6118
12.7%
ValueCountFrequency (%)
6118
12.7%
5124
13.3%
4142
15.3%
3143
15.4%
2133
14.3%
1152
16.4%
0117
12.6%

Lat
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct570
Distinct (%)77.1%
Missing190
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean-1.180784844
Minimum-85
Maximum88.5
Zeros1
Zeros (%)0.1%
Negative374
Negative (%)40.3%
Memory size7.4 KiB
2022-09-22T14:14:10.621342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-85
5-th percentile-62.9
Q1-31
median-1.2
Q328.35
95-th percentile58.07
Maximum88.5
Range173.5
Interquartile range (IQR)59.35

Descriptive statistics

Standard deviation37.56220436
Coefficient of variation (CV)-31.81121823
Kurtosis-0.8044789951
Mean-1.180784844
Median Absolute Deviation (MAD)29.8
Skewness0.01816008652
Sum-872.6
Variance1410.919197
MonotonicityNot monotonic
2022-09-22T14:14:10.813416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
284
 
0.4%
18.93
 
0.3%
46.93
 
0.3%
-50.23
 
0.3%
-24.43
 
0.3%
-443
 
0.3%
21.93
 
0.3%
-31.13
 
0.3%
-41.83
 
0.3%
-21.53
 
0.3%
Other values (560)708
76.2%
(Missing)190
 
20.5%
ValueCountFrequency (%)
-851
0.1%
-83.71
0.1%
-81.11
0.1%
-79.81
0.1%
-78.32
0.2%
-75.81
0.1%
-75.41
0.1%
-72.51
0.1%
-71.71
0.1%
-71.51
0.1%
ValueCountFrequency (%)
88.51
0.1%
86.71
0.1%
82.51
0.1%
82.31
0.1%
801
0.1%
78.71
0.1%
76.91
0.1%
76.71
0.1%
76.61
0.1%
761
0.1%

Long
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct670
Distinct (%)90.7%
Missing190
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean3.348173207
Minimum-179.7
Maximum180
Zeros0
Zeros (%)0.0%
Negative361
Negative (%)38.9%
Memory size7.4 KiB
2022-09-22T14:14:11.027315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-179.7
5-th percentile-163.03
Q1-83.95
median3.6
Q395.05
95-th percentile162.02
Maximum180
Range359.7
Interquartile range (IQR)179

Descriptive statistics

Standard deviation103.9659935
Coefficient of variation (CV)31.0515577
Kurtosis-1.177457618
Mean3.348173207
Median Absolute Deviation (MAD)89.7
Skewness-0.0576328173
Sum2474.3
Variance10808.92781
MonotonicityNot monotonic
2022-09-22T14:14:11.219533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-64.64
 
0.4%
1753
 
0.3%
-37.23
 
0.3%
213
 
0.3%
56.43
 
0.3%
-147.62
 
0.2%
-52.22
 
0.2%
-31.72
 
0.2%
25.72
 
0.2%
51.52
 
0.2%
Other values (660)713
76.7%
(Missing)190
 
20.5%
ValueCountFrequency (%)
-179.71
0.1%
-179.31
0.1%
-178.51
0.1%
-178.31
0.1%
-178.11
0.1%
-176.91
0.1%
-176.21
0.1%
-1761
0.1%
-175.91
0.1%
-175.81
0.1%
ValueCountFrequency (%)
1801
 
0.1%
179.71
 
0.1%
178.51
 
0.1%
177.61
 
0.1%
175.81
 
0.1%
175.51
 
0.1%
175.32
0.2%
1753
0.3%
174.91
 
0.1%
174.41
 
0.1%

Interactions

2022-09-22T14:13:59.141283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T14:13:14.074382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-09-22T14:13:58.803082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-22T14:14:11.411998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-22T14:14:11.752580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-22T14:14:12.082745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-22T14:14:12.412760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-22T14:14:02.134841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-22T14:14:02.757338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-22T14:14:03.103861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-22T14:14:03.381203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Peak Brightness Date/Time (UT)Latitude (deg.)Longitude (deg.)Altitude (km)Velocity (km/s)vxvyvzTotal Radiated Energy (J)Calculated Total Impact Energy (kt)dateyearquartermonthdaytimehourminutesecondweekdayLatLong
02022-08-21 16:35:496.3S51.5E37.0NaNNaNNaNNaN2.100000e+100.0762022-08-212022382116:35:491635496-6.351.5
12022-08-14 07:39:1835.0S78.4E55.5NaNNaNNaNNaN4.680000e+111.2002022-08-142022381407:39:18739186-35.078.4
22022-07-28 01:36:086.0S86.9W37.529.9-17.123.5-7.22.510000e+110.6802022-07-282022372801:36:0813683-6.0-86.9
32022-07-27 04:41:3044.8S2.9W38.119.8-6.117.76.55.240000e+111.3002022-07-272022372704:41:30441302-44.8-2.9
42022-07-25 07:28:1740.5S76.6E33.615.2-2.1-2.214.98.700000e+100.2702022-07-252022372507:28:17728170-40.576.6
52022-07-22 00:16:1923.3S20.5W32.717.4-7.115.5-3.36.000000e+100.1902022-07-222022372200:16:19016194-23.3-20.5
62022-07-20 10:56:5343.0S59.6W32.216.0-0.915.6-3.68.600000e+100.2702022-07-202022372010:56:531056532-43.0-59.6
72022-07-08 01:36:3721.3N130.1E22.020.217.9-4.1-8.32.700000e+100.0952022-07-08202237801:36:3713637421.3130.1
82022-07-07 01:49:2641.7S175.0E35.0NaNNaNNaNNaN7.340000e+111.8002022-07-07202237701:49:26149263-41.7175.0
92022-06-30 04:02:5731.9S12.9W56.0NaNNaNNaNNaN9.100000e+100.2802022-06-302022263004:02:5742573-31.9-12.9

Last rows

Peak Brightness Date/Time (UT)Latitude (deg.)Longitude (deg.)Altitude (km)Velocity (km/s)vxvyvzTotal Radiated Energy (J)Calculated Total Impact Energy (kt)dateyearquartermonthdaytimehourminutesecondweekdayLatLong
9191994-08-15 23:16:48NaNNaNNaNNaNNaNNaNNaN1.640000e+110.4701994-08-151994381523:16:482316480NaNNaN
9201994-06-15 00:02:2645.0N73.5WNaNNaNNaNNaNNaN4.100000e+100.1401994-06-151994261500:02:260226245.0-73.5
9211994-06-03 20:48:42NaNNaNNaNNaNNaNNaNNaN5.090000e+111.3001994-06-03199426320:48:422048424NaNNaN
9221994-05-29 09:30:5852.8N2.3ENaNNaNNaNNaNNaN3.840000e+111.0001994-05-291994252909:30:5893058652.82.3
9231994-02-01 22:38:092.7N164.1ENaNNaNNaNNaNNaN1.820000e+1330.0001994-02-01199412122:38:092238912.7164.1
9241993-11-29 17:48:4126.5N78.3ENaNNaNNaNNaNNaN2.600000e+100.0921993-11-2919934112917:48:41174841026.578.3
9251993-10-31 03:39:2751.3N100.9WNaNNaNNaNNaNNaN4.000000e+100.1301993-10-3119934103103:39:2733927651.3-100.9
9261991-10-04 09:22:4778.7N6.3ENaNNaNNaNNaNNaN5.500000e+111.4001991-10-041991410409:22:4792247478.76.3
9271990-10-01 03:51:477.5N142.8ENaNNaNNaNNaNNaN2.500000e+125.2001990-10-011990410103:51:473514707.5142.8
9281988-04-15 03:03:104.1S124.3ENaNNaNNaNNaNNaN7.580000e+1214.0001988-04-151988241503:03:1033104-4.1124.3